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Bayes model averaging of cyclical decompositions in economic time series

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  • Kleijn, R.H.
  • van Dijk, H.K.

Abstract

A flexible decomposition of a time series into stochastic cycles under possible non-stationarity is specified, providing both a useful data analysis tool and a very wide model class. A Bayes procedure using Markov Chain Monte Carlo (MCMC) is introduced with a model averaging approach which explicitly deals with the uncertainty on the appropriate number of cycles. The convergence of the MCMC method is substantially accelerated through a convenient reparametrization based on a hierarchical structure of variances in a state space model. The model and corresponding inferential procedure are applied to simulated data and to economic time series like industrial production, unemployment and real exchange rates. We derive the implied posterior distributions of model parameters and some relevant functions thereof, shedding light on a wide range of key features of each economic time series.

Suggested Citation

  • Kleijn, R.H. & van Dijk, H.K., 2003. "Bayes model averaging of cyclical decompositions in economic time series," Econometric Institute Research Papers EI 2003-48, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:1080
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    References listed on IDEAS

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    Cited by:

    1. Annalisa Cadonna & Sylvia Fruhwirth-Schnatter & Peter Knaus, 2019. "Triple the gamma -- A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models," Papers 1912.03100, arXiv.org.
    2. Harvey, Andrew C. & Trimbur, Thomas M. & Van Dijk, Herman K., 2007. "Trends and cycles in economic time series: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 618-649, October.
    3. Macaro, Christian, 2010. "Bayesian non-parametric signal extraction for Gaussian time series," Journal of Econometrics, Elsevier, vol. 157(2), pages 381-395, August.

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